Semi-Supervised Person Re-Identification

Semi-Supervised Person Re-Identification (Semi-Supervised Person Re-ID) is a task in the field of computer vision that aims to match pedestrian identities across camera views using a small amount of labeled data and a large amount of unlabeled data. The goal is to improve the accuracy of pedestrian image recognition under different environments and viewpoints by optimizing feature representation and learning strategies, thereby reducing the reliance on large-scale labeled datasets. This technology holds significant value in applications such as intelligent surveillance, security prevention, and urban traffic management, effectively enhancing the practicality and cost-effectiveness of the systems.